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- W2904377564 abstract "Multi-view learning has been explored in various applications such as bioinformatics, natural language processing and multimedia analysis. Often multi-view learning methods commonly assume that full feature matrices or kernel matrices for all views are available. However, in partial data analytics, it is common that information from some sources is not available or missing for some data-points. Such lack of information can be categorized into two types. (1) Incomplete view: information of a data-point is partially missing in some views. (2) Missing view: information of a data-point is entirely missing in some views, but information for that data-point is fully available in other views (no partially missing data-point in a view)." @default.
- W2904377564 created "2018-12-22" @default.
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- W2904377564 date "2018-11-27" @default.
- W2904377564 modified "2023-09-25" @default.
- W2904377564 title "Multi-View Data Completion" @default.
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- W2904377564 doi "https://doi.org/10.1007/978-3-030-01872-6_1" @default.
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